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 Graph Embedding with Rich Information through Bipartite Heterogeneous Network

Graph embedding has attracted increasing attention due to its critical application in social network analysis. Most existing algorithms for graph embedding only rely on the typology information and fail to use the copious information in nodes as well as edges. As a result, their performance for man…


 Query-based Attention CNN for Text Similarity Map

 

In this paper, we introduce Query-based Attention CNN(QACNN) for Text Similarity Map, an end-to-end neural network for question answering. This network is composed of compare mechanism, two-staged CNN architecture with attention mechanism, and a prediction layer. First, the compare mechanism compar…


 Learning Social Image Embedding with Deep Multimodal Attention Networks

 

Learning social media data embedding by deep models has attracted extensive research interest as well as boomed a lot of applications, such as link prediction, classification, and cross-modal search. However, for social images which contain both link information and multimodal contents (e.g., text …


 Constructing Datasets for Multi-hop Reading Comprehension Across Documents

Most Reading Comprehension methods limit themselves to queries which can be answered using a single sentence, paragraph, or document. Enabling models to combine disjoint pieces of textual evidence would extend the scope of machine comprehension methods, but currently there exist no resources to tra…


 Fishing for Clickbaits in Social Images and Texts with Linguistically-Infused Neural Network Models

   

This paper presents the results and conclusions of our participation in the Clickbait Challenge 2017 on automatic clickbait detection in social media. We first describe linguistically-infused neural network models and identify informative representations to predict the level of clickbaiting present…


 A retrieval-based dialogue system utilizing utterance and context embeddings

Finding semantically rich and computer-understandable representations for textual dialogues, utterances and words is crucial for dialogue systems (or conversational agents), as their performance mostly depends on understanding the context of conversations. Recent research aims at finding distribute…


 Sequence stacking using dual encoder Seq2Seq recurrent networks

 

A widely studied non-polynomial (NP) hard problem lies in finding a route between the two nodes of a graph. Often meta-heuristics algorithms such as $A^{*}$ are employed on graphs with a large number of nodes. Here, we propose a deep recurrent neural network architecture based on the Sequence-2-Seq…


 Learning to Rank Question-Answer Pairs using Hierarchical Recurrent Encoder with Latent Topic Clustering

  

In this paper, we propose a novel end-to-end neural architecture for ranking answers from candidates that adapts a hierarchical recurrent neural network and a latent topic clustering module. With our proposed model, a text is encoded to a vector representation from an word-level to a chunk-level to…


 Geo-referencing Place from Everyday Natural Language Descriptions

 

Natural language place descriptions in everyday communication provide a rich source of spatial knowledge about places. An important step to utilize such knowledge in information systems is geo-referencing all the places referred to in these descriptions. Current techniques for geo-referencing place…


 Checkpoint Ensembles: Ensemble Methods from a Single Training Process

   

We present the checkpoint ensembles method that can learn ensemble models on a single training process. Although checkpoint ensembles can be applied to any parametric iterative learning technique, here we focus on neural networks. Neural networks’ composable and simple neurons make it possibl…


 Data Science 101: Sentiment Analysis in R Tutorial

 

Welcome back to Data Science 101! Do you have text data? Do you want to figure out whether the opinions expressed in it are positive or negative? Then you’ve come to the right place! Today, we’re going to get you up to speed on sentiment analysis. By the end of this tutorial you will: U…


 How Important is Syntactic Parsing Accuracy? An Empirical Evaluation on Rule-Based Sentiment Analysis

  

Syntactic parsing, the process of obtaining the internal structure of sentences in natural languages, is a crucial task for artificial intelligence applications that need to extract meaning from natural language text or speech. Sentiment analysis is one example of application for which parsing has …


 Personalized Fuzzy Text Search Using Interest Prediction and Word Vectorization

 

In this paper we study the personalized text search problem. The keyword based search method in conventional algorithms has a low efficiency in understanding users’ intention since the semantic meaning, user profile, user interests are not always considered. Firstly, we propose a novel text s…


 Upper Bound of Bayesian Generalization Error in Non-negative Matrix Factorization

  

Non-negative matrix factorization (NMF) is a new knowledge discovery method that is used for text mining, signal processing, bioinformatics, and consumer analysis. However, its basic property as a learning machine is not yet clarified, as it is not a regular statistical model, resulting that theore…


 A Neural Comprehensive Ranker (NCR) for Open-Domain Question Answering

This paper proposes a novel neural machine reading model for open-domain question answering at scale. Existing machine comprehension models typically assume that a short piece of relevant text containing answers is already identified and given to the models, from which the models are designed to ex…


 KeyVec: Key-semantics Preserving Document Representations

Previous studies have demonstrated the empirical success of word embeddings in various applications. In this paper, we investigate the problem of learning distributed representations for text documents which many machine learning algorithms take as input for a number of NLP tasks. We propose a neur…


 Extracting Ontological Knowledge from Textual Descriptions through Grammar-based Transformation

 

Authoring of OWL-DL ontologies is intellectually challenging and to make this process simpler, many systems accept natural language text as input. A text-based ontology authoring approach can be successful only when it is combined with an effective method for extracting ontological axioms from text…


 Object-oriented Neural Programming (OONP) for Document Understanding

 

We propose Object-oriented Neural Programming (OONP), a framework for semantically parsing documents in specific domains. Basically, OONP reads a document and parses it into a predesigned object-oriented data structure (referred to as ontology in this paper) that reflects the domain-specific semant…


 Glass-Box Program Synthesis: A Machine Learning Approach

Recently proposed models which learn to write computer programs from data use either input/output examples or rich execution traces. Instead, we argue that a novel alternative is to use a glass-box loss function, given as a program itself that can be directly inspected. Glass-box optimization cover…


 EZLearn: Exploiting Organic Supervision in Large-Scale Data Annotation

 

We propose Extreme Zero-shot Learning (EZLearn) for classifying data into potentially thousands of classes, with zero labeled examples. The key insight is to leverage the abundant unlabeled data together with two sources of organic supervision: a lexicon for the annotation classes, and text descrip…


 HDLTex: Hierarchical Deep Learning for Text Classification

 

The continually increasing number of documents produced each year necessitates ever improving information processing methods for searching, retrieving, and organizing text. Central to these information processing methods is document classification, which has become an important application for supe…


 Long Text Generation via Adversarial Training with Leaked Information

     

Automatically generating coherent and semantically meaningful text has many applications in machine translation, dialogue systems, image captioning, etc. Recently, by combining with policy gradient, Generative Adversarial Nets (GAN) that use a discriminative model to guide the training of the gener…


 Piecewise Latent Variables for Neural Variational Text Processing

 

Advances in neural variational inference have facilitated the learning of powerful directed graphical models with continuous latent variables, such as variational autoencoders. The hope is that such models will learn to represent rich, multi-modal latent factors in real-world data, such as natural …


 Attention-based Wav2Text with Feature Transfer Learning

  

Conventional automatic speech recognition (ASR) typically performs multi-level pattern recognition tasks that map the acoustic speech waveform into a hierarchy of speech units. But, it is widely known that information loss in the earlier stage can propagate through the later stages. After the resur…


 Deconvolutional Latent-Variable Model for Text Sequence Matching

  

A latent-variable model is introduced for text matching, inferring sentence representations by jointly optimizing generative and discriminative objectives. To alleviate typical optimization challenges in latent-variable models for text, we employ deconvolutional networks as the sequence decoder (ge…